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Testing the Accuracy of a Bedside Screening Tool Framework to Clinical Records for Identification of Patients at Risk of Malnutrition in a Rural Setting: An Exploratory Study

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The Global Obesity Centre, Institute for Health Transformation, Deakin University, Geelong, VIC 3220, Australia
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Research Unit, Colac Area Health, Colac, VIC 3250, Australia
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Deakin Rural Health, School of Medicine Faculty of Health, Deakin University, Geelong, VIC 3220, Australia
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School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
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Engagement and Co-Design Hub, The University of Sydney, Camperdown, NSW 2006, Australia
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Prevention Research Collaboration, Charles Perkins Centre, Sydney School Public Health, The University of Sydney, Sydney, NSW 2006, Australia
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Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC 3220, Australia
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Biostatistics Unit, Faculty of Health, Deakin University, Geelong, VIC 3220, Australia
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Author to whom correspondence should be addressed.
Academic Editor: Patrick R.M. Lauwers
Nutrients 2022, 14(1), 205; https://doi.org/10.3390/nu14010205
Received: 13 December 2021 / Revised: 30 December 2021 / Accepted: 31 December 2021 / Published: 2 January 2022
(This article belongs to the Section Clinical Nutrition)
This study aimed to explore the diagnostic accuracy of the Patient-Generated Subjective Global Assessment (PG-SGA) malnutrition risk screening tool when used to score patients based on their electronic medical records (EMR), compared to bedside screening interviews. In-patients at a rural health service were screened at the bedside (n = 50) using the PG-SGA, generating a bedside score. Clinical notes within EMRs were then independently screened by blinded researchers. The accuracy of the EMR score was assessed against the bedside score using area under the receiver operating curve (AUC), sensitivity, and specificity. Participants were 62% female and 32% had conditions associated with malnutrition, with a mean age of 70.6 years (SD 14.9). The EMR score had moderate diagnostic accuracy relative to PG-SGA bedside screen, AUC 0.74 (95% CI: 0.59–0.89). The accuracy, specificity and sensitivity of the EMR score was highest for patients with a score of 7, indicating EMR screen is more likely to detect patients at risk of malnutrition. This exploratory study showed that applying the PG-SGA screening tool to EMRs had enough sensitivity and specificity for identifying patients at risk of malnutrition to warrant further exploration in low-resource settings. View Full-Text
Keywords: rural; malnutrition risk; malnutrition screening; rural health services rural; malnutrition risk; malnutrition screening; rural health services
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MDPI and ACS Style

Alston, L.; Green, M.; Nichols, M.; Partridge, S.R.; Buccheri, A.; Bolton, K.A.; Versace, V.L.; Field, M.; Launder, A.J.; Lily, A.; Allender, S.; Orellana, L. Testing the Accuracy of a Bedside Screening Tool Framework to Clinical Records for Identification of Patients at Risk of Malnutrition in a Rural Setting: An Exploratory Study. Nutrients 2022, 14, 205. https://doi.org/10.3390/nu14010205

AMA Style

Alston L, Green M, Nichols M, Partridge SR, Buccheri A, Bolton KA, Versace VL, Field M, Launder AJ, Lily A, Allender S, Orellana L. Testing the Accuracy of a Bedside Screening Tool Framework to Clinical Records for Identification of Patients at Risk of Malnutrition in a Rural Setting: An Exploratory Study. Nutrients. 2022; 14(1):205. https://doi.org/10.3390/nu14010205

Chicago/Turabian Style

Alston, Laura, Megan Green, Melanie Nichols, Stephanie R. Partridge, Alison Buccheri, Kristy A. Bolton, Vincent L. Versace, Michael Field, Ambrose J. Launder, Amy Lily, Steven Allender, and Liliana Orellana. 2022. "Testing the Accuracy of a Bedside Screening Tool Framework to Clinical Records for Identification of Patients at Risk of Malnutrition in a Rural Setting: An Exploratory Study" Nutrients 14, no. 1: 205. https://doi.org/10.3390/nu14010205

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